A WLAV-based Robust Hybrid State Estimation using Circuit-theoretic Approach

For reliable and secure power grid operation, AC state-estimation (ACSE) must provide certain guarantees of convergence while being resilient against bad-data. This paper develops a circuit-theoretic weighted least absolute value (WLAV) based hybrid ACSE that satisfies these needs to overcome some of the limitations of existing ACSE methods. Hybrid refers to the inclusion of RTU and PMU measurement data, and the use of the LAV objective function enables automatic rejection of bad data while providing clear identification of suspicious measurements from the sparse residual vector. Taking advantage of linear construction of the measurement models in circuit-theoretic approach, the proposed hybrid SE is formulated as a LP problem with guaranteed convergence. To address efficiency, we further develop problem-specific heuristics for fast convergence. To validate the efficacy of the proposed approach, we run ACSE on large cases and compare the results against WLS-based algorithms. We further demonstrate the advantages of our solution methodology over standard commercial LP solvers through comparison of runtime and convergence performance.

[1]  Gabriela Hug,et al.  An Equivalent Circuit Formulation for Power System State Estimation including PMUs , 2018, 2018 North American Power Symposium (NAPS).

[2]  M. Vidyasagar,et al.  Bad Data Rejection Properties of Weughted Least Absolute Value Techniques Applied to Static State Estimation , 1982, IEEE Transactions on Power Apparatus and Systems.

[3]  Yang Weng,et al.  Convexification of bad data and topology error detection and identification problems in AC electric power systems , 2015 .

[4]  Stephen P. Boyd,et al.  An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression , 2007, J. Mach. Learn. Res..

[5]  Ali Abur,et al.  LAV Based Robust State Estimation for Systems Measured by PMUs , 2014, IEEE Transactions on Smart Grid.

[6]  Nesa L'abbe Wu,et al.  Linear programming and extensions , 1981 .

[7]  Lamine Mili,et al.  Iteratively reweighted least-squares state estimation through Givens Rotations , 1999 .

[8]  Marko Jereminov,et al.  Equivalent Circuit Formulation for Solving AC Optimal Power Flow , 2019, IEEE Transactions on Power Systems.

[9]  Joel Nothman,et al.  SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.

[10]  Fred C. Schweppe,et al.  Power System Static-State Estimation, Part I: Exact Model , 1970 .

[11]  Ali Abur,et al.  Robust State Estimation Against Measurement and Network Parameter Errors , 2018, IEEE Transactions on Power Systems.

[12]  P. Rousseeuw,et al.  Least median of squares estimation in power systems , 1991 .

[13]  Soummya Kar,et al.  A Circuit-Theoretic Approach to State Estimation , 2020, 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe).

[14]  Gabriela Hug,et al.  Robust Power Flow and Three-Phase Power Flow Analyses , 2018, IEEE Transactions on Power Systems.

[15]  A. Monticelli,et al.  Reliable Bad Data Processing for Real-Time State Estimation , 1983, IEEE Power Engineering Review.

[16]  M. E. El-Hawary,et al.  Measurement of power systems voltage and flicker levels for power quality analysis: a static LAV state estimation based algorithm , 2000 .

[17]  F. Alvarado,et al.  Constrained LAV state estimation using penalty functions , 1997 .

[18]  E. Handschin,et al.  Bad data analysis for power system state estimation , 1975, IEEE Transactions on Power Apparatus and Systems.